Brucellosis is an infectious and contagious disease that profoundly impacts public health. However, in many countries, disease prevention is restricted to the vaccination of calves, and there is no prophylactic strategy for pregnant heifers and cows. The aim of this study was to evaluate the safety of the rough strain vaccine against brucellosis in pregnant cattle. Crossbred cows (N = 96) at three gestational periods (early, mid, or late pregnancy) were randomly allocated into the vaccine treatment group or to the control group. We then compared the percentage of pregnancies reaching full term, live calves 60 days after delivery, and seropositive calves. There was no effect of vaccination in any of the gestational periods on the evaluation endpoints. In conclusion, vaccination against brucellosis with the rough strain is safe for pregnant cattle at all gestational periods.
相似文献BACKGROUND
The yellow-legged hornet (Vespa velutina) is native to Southeast Asia and is an invasive alien species of concern in many countries. More effective management of populations of V. velutina could be achieved through more widespread and intensive monitoring in the field, however current methods are labor intensive and costly. To address this issue, we have assessed the performance of an optical sensor combined with a machine learning model to classify V. velutina and native wasps/hornets and bees. Our aim is to use the results of the present work as a step towards the development of a monitoring solution for V. velutina in the field.RESULTS
We recorded a total 935 flights from three bee species: Apis mellifera, Bombus terrestris and Osmia bicornis; and four wasp/hornet species: Polistes dominula, Vespula germanica, Vespa crabro and V. velutina. The machine learning model achieved an average accuracy for species classification of 80.1 ± 13.9% and 74.5 ± 7.0% for V. velutina. V. crabro had the highest level of misclassification, confused mainly with V. velutina and P. dominula. These results were obtained using a 14-value peak and valley feature derived from the wingbeat power spectral density.CONCLUSION
This study demonstrates that the wingbeat recordings from a flying insect sensor can be used with machine learning methods to differentiate V. velutina from six other Hymenoptera species in the laboratory and this knowledge could be used to help develop a tool for use in integrated invasive alien species management programs. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. 相似文献![点击此处可从《Pest management science》网站下载免费的PDF全文](/ch/ext_images/free.gif)